2 citations · 4 across the 13 of their papers we have counts for
13 papers
TraceML: What Auto-Research Agents Miss in Long-Horizon ML Development
Jiarui Yan, Weiwei Sun, Sijie Li +2
Auto-research agents now run machine-learning development unattended for hours, revising data pipelines, models, and validation from their own feedback, yet on most competitions th…
ReproRepo: Scaling Reproducibility Audits with GitHub Repository Issues
Shanda Li, Qiuhong Anna Wei, Jingwu Tang +5
Reproducing research results from papers and released code is central to scientific progress. Existing works have introduced benchmarks to evaluate whether LLM agents can assist wi…
Spend Less, Fit Better: Budget-Efficient Scaling Law Fitting via Active Experiment Selection
Sijie Li, Shanda Li, Haowei Lin +3
Scaling laws are used to plan multi-million-dollar training runs, but fitting those laws can itself cost millions. In modern large-scale workflows, assembling a sufficiently inform…
Learn Hard Problems During RL with Reference Guided Fine-tuning
Yangzhen Wu, Shanda Li, Zixin Wen +5
Reinforcement learning (RL) for mathematical reasoning can suffer from reward sparsity: for challenging problems, LLM fails to sample any correct trajectories, preventing RL from r…
Maximal Update Parametrization and Zero-Shot Hyperparameter Transfer for Fourier Neural Operators
Shanda Li, Shinjae Yoo, Yiming Yang
Fourier Neural Operators (FNOs) offer a principled approach for solving complex partial differential equations (PDEs). However, scaling them to handle more complex PDEs requires in…
Sample Complexity and Representation Ability of Test-time Scaling Paradigms
Baihe Huang, Shanda Li, Tianhao Wu +5
Test-time scaling paradigms have significantly advanced the capabilities of large language models (LLMs) on complex tasks. Despite their empirical success, theoretical understandin…
CoMind: Towards Community-Driven Agents for Machine Learning Engineering
Sijie Li, Weiwei Sun, Shanda Li +2
Large language model (LLM) agents show promise in automating machine learning (ML) engineering. However, existing agents typically operate in isolation on a given research problem,…
FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization
Shengyu Feng, Weiwei Sun, Shanda Li +2
Machine learning (ML) has shown promise for tackling combinatorial optimization (CO), but much of the reported progress relies on small-scale, synthetic benchmarks that fail to cap…
CodePDE: An Inference Framework for LLM-driven PDE Solver Generation
Shanda Li, Tanya Marwah, Junhong Shen +4
Partial differential equations (PDEs) are fundamental to modeling physical systems, yet solving them remains a complex challenge. Traditional numerical solvers rely on expert knowl…
CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial Optimization
Weiwei Sun, Shengyu Feng, Shanda Li +1
Although LLM-based agents have attracted significant attention in domains such as software engineering and machine learning research, their role in advancing combinatorial optimiza…
TFG-Flow: Training-free Guidance in Multimodal Generative Flow
Haowei Lin, Shanda Li, Haotian Ye +4
Given an unconditional generative model and a predictor for a target property (e.g., a classifier), the goal of training-free guidance is to generate samples with desirable target…
Functional Interpolation for Relative Positions Improves Long Context Transformers
Shanda Li, Chong You, Guru Guruganesh +7
Preventing the performance decay of Transformers on inputs longer than those used for training has been an important challenge in extending the context length of these models. Thou…
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers
Krzysztof Marcin Choromanski, Shanda Li, Valerii Likhosherstov +7
We propose a new class of linear Transformers called FourierLearner-Transformers (FLTs), which incorporate a wide range of relative positional encoding mechanisms (RPEs). These inc…